By 2020 roughly 200 million people worldwide will suffer from retinal
diseases such as retinitis pigmentosa (RP) and age-related macular degeneration (AMD).
RP is identified by a progressive degeneration of photoreceptors beginning in the peripheral
retina, whereas photoreceptor degeneration in AMD begins in the macula (see figure below).

Analogous to cochlear implants, the goal of electronic retinal prostheses is to produce
meaningful visual information by electrically stimulating remaining retinal cells.
Several types of retinal prostheses are currently under development, including
epiretinal prostheses (where the implant is placed on the ganglion cell surface within
the vitreous space),
subretinal prostheses (where the implant is placed between bipolar cells and the retinal
pigmented epithelium),
and suprachoroidal prostheses (where the implant is placed either between the choroid and
the sclera or contained within the sclera).
All of these approaches are similar in that light from the visual scene is captured and
transformed into electrical pulses delivered through electrodes to stimulate the retina.

However, these devices do not restore anything resembling natural vision:
Interactions between the electronics and the underlying neurophysiology result
in significant distortions of the perceptual experience.
For example, epiretinal prostheses face the challenge that they do not only activate
ganglion cell bodies, but also their axons. This leads to a perceptual ‘smearing’ of
the stimulus, producing percepts that resemble ‘comet streaks’:

Simulations of perceptual distortions as a result of axonal stimulation.
An image of a Snellen chart is overlaid on the retinal surface (left).
The position of the retinal implant (subtending 12 deg) is shown by a red
dashed box.
The predicted effect of axonal stimulation on the image is shown for different
values of lambda (describing the activation sensitivity of a passing axon fiber
as a function of distance).
Source: Fine
and Boynton 2015.

We have developed a computer model that has the goal of predicting the perceptual
experience of retinal prosthesis patients.
The model was developed using a variety of patient data describing the brightness
and shape of phosphenes elicited by stimulating single electrodes, and validated
against an independent set of behavioral measures examining spatiotemporal
interactions across multiple electrodes.

The model takes as input a series of (simulated) electrical pulse trains—one pulse
train per electrode in the array—and converts them into an image sequence that
corresponds to the predicted perceptual experience of a patient:

The following are two example simulations illustrating the predicted perceptual experience of a retinal
prosthesis patient:

Predicted percept

Input stimulus

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Predicted percept

Input stimulus

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The videos show that patients may see fuzzy, comet-like shapes or blurred
outlines, or experience temporary disappearances if the object moves too fast.

Simulations such as these, which provide an insight into the perceptual outcomes of prosthetic vision, are likely to be critical for providing realistic estimates of prosthetic vision, providing regulatory bodies with guidance into what sort of visual tests are appropriate for evaluating prosthetic performance, and improving current and future technology.